library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.1 ✔ readr 2.1.4
## ✔ forcats 1.0.0 ✔ stringr 1.5.0
## ✔ ggplot2 3.4.1 ✔ tibble 3.2.1
## ✔ lubridate 1.9.2 ✔ tidyr 1.3.0
## ✔ purrr 1.0.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
raw_data <- read.csv("/Users/ibhargava/Desktop/globalterrorismdb_0718dist.csv")
data2 <- raw_data
summary(raw_data)
## eventid iyear imonth iday
## Min. :1.970e+11 Min. :1970 Min. : 0.000 Min. : 0.00
## 1st Qu.:1.991e+11 1st Qu.:1991 1st Qu.: 4.000 1st Qu.: 8.00
## Median :2.009e+11 Median :2009 Median : 6.000 Median :15.00
## Mean :2.003e+11 Mean :2003 Mean : 6.467 Mean :15.51
## 3rd Qu.:2.014e+11 3rd Qu.:2014 3rd Qu.: 9.000 3rd Qu.:23.00
## Max. :2.017e+11 Max. :2017 Max. :12.000 Max. :31.00
##
## approxdate extended resolution country
## Length:181691 Min. :0.00000 Length:181691 Min. : 4
## Class :character 1st Qu.:0.00000 Class :character 1st Qu.: 78
## Mode :character Median :0.00000 Mode :character Median : 98
## Mean :0.04535 Mean : 132
## 3rd Qu.:0.00000 3rd Qu.: 160
## Max. :1.00000 Max. :1004
##
## country_txt region region_txt provstate
## Length:181691 Min. : 1.000 Length:181691 Length:181691
## Class :character 1st Qu.: 5.000 Class :character Class :character
## Mode :character Median : 6.000 Mode :character Mode :character
## Mean : 7.161
## 3rd Qu.:10.000
## Max. :12.000
##
## city latitude longitude specificity
## Length:181691 Min. :-53.16 Min. :-86185896 Min. :1.000
## Class :character 1st Qu.: 11.51 1st Qu.: 5 1st Qu.:1.000
## Mode :character Median : 31.47 Median : 43 Median :1.000
## Mean : 23.50 Mean : -459 Mean :1.451
## 3rd Qu.: 34.69 3rd Qu.: 69 3rd Qu.:1.000
## Max. : 74.63 Max. : 179 Max. :5.000
## NA's :4556 NA's :4557 NA's :6
## vicinity location summary crit1
## Min. :-9.0000 Length:181691 Length:181691 Min. :0.0000
## 1st Qu.: 0.0000 Class :character Class :character 1st Qu.:1.0000
## Median : 0.0000 Mode :character Mode :character Median :1.0000
## Mean : 0.0683 Mean :0.9885
## 3rd Qu.: 0.0000 3rd Qu.:1.0000
## Max. : 1.0000 Max. :1.0000
##
## crit2 crit3 doubtterr alternative
## Min. :0.0000 Min. :0.0000 Min. :-9.0000 Min. :1.00
## 1st Qu.:1.0000 1st Qu.:1.0000 1st Qu.: 0.0000 1st Qu.:1.00
## Median :1.0000 Median :1.0000 Median : 0.0000 Median :1.00
## Mean :0.9931 Mean :0.8757 Mean :-0.5232 Mean :1.29
## 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.: 0.0000 3rd Qu.:1.00
## Max. :1.0000 Max. :1.0000 Max. : 1.0000 Max. :5.00
## NA's :1 NA's :152680
## alternative_txt multiple success suicide
## Length:181691 Min. :0.0000 Min. :0.0000 Min. :0.00000
## Class :character 1st Qu.:0.0000 1st Qu.:1.0000 1st Qu.:0.00000
## Mode :character Median :0.0000 Median :1.0000 Median :0.00000
## Mean :0.1378 Mean :0.8896 Mean :0.03651
## 3rd Qu.:0.0000 3rd Qu.:1.0000 3rd Qu.:0.00000
## Max. :1.0000 Max. :1.0000 Max. :1.00000
## NA's :1
## attacktype1 attacktype1_txt attacktype2 attacktype2_txt
## Min. :1.000 Length:181691 Min. :1.00 Length:181691
## 1st Qu.:2.000 Class :character 1st Qu.:2.00 Class :character
## Median :3.000 Mode :character Median :2.00 Mode :character
## Mean :3.248 Mean :3.72
## 3rd Qu.:3.000 3rd Qu.:7.00
## Max. :9.000 Max. :9.00
## NA's :175377
## attacktype3 attacktype3_txt targtype1 targtype1_txt
## Min. :1.00 Length:181691 Min. : 1.00 Length:181691
## 1st Qu.:2.00 Class :character 1st Qu.: 3.00 Class :character
## Median :7.00 Mode :character Median : 4.00 Mode :character
## Mean :5.25 Mean : 8.44
## 3rd Qu.:7.00 3rd Qu.:14.00
## Max. :8.00 Max. :22.00
## NA's :181263
## targsubtype1 targsubtype1_txt corp1 target1
## Min. : 1.00 Length:181691 Length:181691 Length:181691
## 1st Qu.: 22.00 Class :character Class :character Class :character
## Median : 35.00 Mode :character Mode :character Mode :character
## Mean : 46.97
## 3rd Qu.: 74.00
## Max. :113.00
## NA's :10373
## natlty1 natlty1_txt targtype2 targtype2_txt
## Min. : 4.0 Length:181691 Min. : 1.00 Length:181691
## 1st Qu.: 83.0 Class :character 1st Qu.: 4.00 Class :character
## Median : 101.0 Mode :character Median :14.00 Mode :character
## Mean : 127.7 Mean :10.25
## 3rd Qu.: 173.0 3rd Qu.:14.00
## Max. :1004.0 Max. :22.00
## NA's :1559 NA's :170547
## targsubtype2 targsubtype2_txt corp2 target2
## Min. : 1.00 Length:181691 Length:181691 Length:181691
## 1st Qu.: 34.00 Class :character Class :character Class :character
## Median : 67.00 Mode :character Mode :character Mode :character
## Mean : 55.31
## 3rd Qu.: 69.00
## Max. :113.00
## NA's :171006
## natlty2 natlty2_txt targtype3 targtype3_txt
## Min. : 4.0 Length:181691 Min. : 1.00 Length:181691
## 1st Qu.: 92.0 Class :character 1st Qu.: 3.00 Class :character
## Median : 98.0 Mode :character Median :14.00 Mode :character
## Mean : 131.2 Mean :10.02
## 3rd Qu.: 182.0 3rd Qu.:14.00
## Max. :1004.0 Max. :22.00
## NA's :170863 NA's :180515
## targsubtype3 targsubtype3_txt corp3 target3
## Min. : 1.00 Length:181691 Length:181691 Length:181691
## 1st Qu.: 33.00 Class :character Class :character Class :character
## Median : 67.00 Mode :character Mode :character Mode :character
## Mean : 55.55
## 3rd Qu.: 73.00
## Max. :113.00
## NA's :180594
## natlty3 natlty3_txt gname gsubname
## Min. : 4.0 Length:181691 Length:181691 Length:181691
## 1st Qu.: 75.0 Class :character Class :character Class :character
## Median : 110.0 Mode :character Mode :character Mode :character
## Mean : 144.6
## 3rd Qu.: 182.0
## Max. :1004.0
## NA's :180544
## gname2 gsubname2 gname3 gsubname3
## Length:181691 Length:181691 Length:181691 Length:181691
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## motive guncertain1 guncertain2 guncertain3
## Length:181691 Min. :0.0000 Min. :0.00 Min. :0.00
## Class :character 1st Qu.:0.0000 1st Qu.:0.00 1st Qu.:0.00
## Mode :character Median :0.0000 Median :0.00 Median :0.00
## Mean :0.0814 Mean :0.27 Mean :0.19
## 3rd Qu.:0.0000 3rd Qu.:1.00 3rd Qu.:0.00
## Max. :1.0000 Max. :1.00 Max. :1.00
## NA's :380 NA's :179736 NA's :181371
## individual nperps nperpcap claimed
## Min. :0.00000 Min. : -99.00 Min. :-99.00 Min. :-9.00
## 1st Qu.:0.00000 1st Qu.: -99.00 1st Qu.: 0.00 1st Qu.: 0.00
## Median :0.00000 Median : -99.00 Median : 0.00 Median : 0.00
## Mean :0.00295 Mean : -65.36 Mean : -1.52 Mean : 0.05
## 3rd Qu.:0.00000 3rd Qu.: 1.00 3rd Qu.: 0.00 3rd Qu.: 0.00
## Max. :1.00000 Max. :25000.00 Max. :406.00 Max. : 1.00
## NA's :71115 NA's :69489 NA's :66120
## claimmode claimmode_txt claim2 claimmode2
## Min. : 1.00 Length:181691 Min. :-9.00 Min. : 1.00
## 1st Qu.: 6.00 Class :character 1st Qu.: 0.00 1st Qu.: 6.00
## Median : 8.00 Mode :character Median : 0.00 Median : 7.00
## Mean : 7.02 Mean : 0.25 Mean : 7.18
## 3rd Qu.: 8.00 3rd Qu.: 1.00 3rd Qu.:10.00
## Max. :10.00 Max. : 1.00 Max. :10.00
## NA's :162608 NA's :179801 NA's :181075
## claimmode2_txt claim3 claimmode3 claimmode3_txt
## Length:181691 Min. :0.00 Min. : 1.00 Length:181691
## Class :character 1st Qu.:0.00 1st Qu.: 4.00 Class :character
## Mode :character Median :0.00 Median : 7.00 Mode :character
## Mean :0.41 Mean : 6.73
## 3rd Qu.:1.00 3rd Qu.: 9.00
## Max. :1.00 Max. :10.00
## NA's :181373 NA's :181558
## compclaim weaptype1 weaptype1_txt weapsubtype1
## Min. :-9.0 Min. : 1.000 Length:181691 Min. : 1.00
## 1st Qu.:-9.0 1st Qu.: 5.000 Class :character 1st Qu.: 5.00
## Median :-9.0 Median : 6.000 Mode :character Median :12.00
## Mean :-6.3 Mean : 6.447 Mean :11.12
## 3rd Qu.: 0.0 3rd Qu.: 6.000 3rd Qu.:16.00
## Max. : 1.0 Max. :13.000 Max. :31.00
## NA's :176852 NA's :20768
## weapsubtype1_txt weaptype2 weaptype2_txt weapsubtype2
## Length:181691 Min. : 1.00 Length:181691 Min. : 1.00
## Class :character 1st Qu.: 5.00 Class :character 1st Qu.: 5.00
## Mode :character Median : 6.00 Mode :character Median : 7.00
## Mean : 6.81 Mean :10.75
## 3rd Qu.: 8.00 3rd Qu.:18.00
## Max. :13.00 Max. :31.00
## NA's :168564 NA's :170149
## weapsubtype2_txt weaptype3 weaptype3_txt weapsubtype3
## Length:181691 Min. : 2.00 Length:181691 Min. : 1.00
## Class :character 1st Qu.: 5.00 Class :character 1st Qu.: 4.00
## Mode :character Median : 6.00 Mode :character Median : 7.00
## Mean : 6.91 Mean :11.64
## 3rd Qu.: 9.00 3rd Qu.:20.00
## Max. :13.00 Max. :28.00
## NA's :179828 NA's :179998
## weapsubtype3_txt weaptype4 weaptype4_txt weapsubtype4
## Length:181691 Min. : 5.00 Length:181691 Min. : 2.00
## Class :character 1st Qu.: 5.00 Class :character 1st Qu.: 3.00
## Mode :character Median : 6.00 Mode :character Median : 9.50
## Mean : 6.25 Mean :10.84
## 3rd Qu.: 6.00 3rd Qu.:16.00
## Max. :12.00 Max. :28.00
## NA's :181618 NA's :181621
## weapsubtype4_txt weapdetail nkill nkillus
## Length:181691 Length:181691 Min. : 0.000 Min. : 0.00
## Class :character Class :character 1st Qu.: 0.000 1st Qu.: 0.00
## Mode :character Mode :character Median : 0.000 Median : 0.00
## Mean : 2.403 Mean : 0.05
## 3rd Qu.: 2.000 3rd Qu.: 0.00
## Max. :1570.000 Max. :1360.00
## NA's :10313 NA's :64446
## nkillter nwound nwoundus nwoundte
## Min. : 0.00 Min. : 0.000 Min. : 0.00 Min. : 0.00
## 1st Qu.: 0.00 1st Qu.: 0.000 1st Qu.: 0.00 1st Qu.: 0.00
## Median : 0.00 Median : 0.000 Median : 0.00 Median : 0.00
## Mean : 0.51 Mean : 3.168 Mean : 0.04 Mean : 0.11
## 3rd Qu.: 0.00 3rd Qu.: 2.000 3rd Qu.: 0.00 3rd Qu.: 0.00
## Max. :500.00 Max. :8191.000 Max. :751.00 Max. :200.00
## NA's :66958 NA's :16311 NA's :64702 NA's :69143
## property propextent propextent_txt propvalue
## Min. :-9.0000 Min. :1.0 Length:181691 Min. : -99
## 1st Qu.: 0.0000 1st Qu.:3.0 Class :character 1st Qu.: -99
## Median : 1.0000 Median :3.0 Mode :character Median : -99
## Mean :-0.5446 Mean :3.3 Mean : 208812
## 3rd Qu.: 1.0000 3rd Qu.:4.0 3rd Qu.: 1000
## Max. : 1.0000 Max. :4.0 Max. :2700000000
## NA's :117626 NA's :142702
## propcomment ishostkid nhostkid nhostkidus
## Length:181691 Min. :-9.00000 Min. : -99.00 Min. :-99.00
## Class :character 1st Qu.: 0.00000 1st Qu.: 1.00 1st Qu.: 0.00
## Mode :character Median : 0.00000 Median : 2.00 Median : 0.00
## Mean : 0.05905 Mean : 4.53 Mean : -0.35
## 3rd Qu.: 0.00000 3rd Qu.: 4.00 3rd Qu.: 0.00
## Max. : 1.00000 Max. :17000.00 Max. : 86.00
## NA's :178 NA's :168119 NA's :168174
## nhours ndays divert kidhijcountry
## Min. :-99.00 Min. : -99.00 Length:181691 Length:181691
## 1st Qu.:-99.00 1st Qu.: -99.00 Class :character Class :character
## Median :-99.00 Median : -99.00 Mode :character Mode :character
## Mean :-46.79 Mean : -32.52
## 3rd Qu.: 0.00 3rd Qu.: 4.00
## Max. :999.00 Max. :2454.00
## NA's :177628 NA's :173567
## ransom ransomamt ransomamtus ransompaid
## Min. :-9.00 Min. : -99 Min. : -99 Min. : -99
## 1st Qu.: 0.00 1st Qu.: 0 1st Qu.: 0 1st Qu.: -99
## Median : 0.00 Median : 15000 Median : 0 Median : 0
## Mean :-0.15 Mean : 3172530 Mean : 578487 Mean : 717944
## 3rd Qu.: 0.00 3rd Qu.: 400000 3rd Qu.: 0 3rd Qu.: 1273
## Max. : 1.00 Max. :1000000000 Max. :132000000 Max. :275000000
## NA's :104310 NA's :180341 NA's :181128 NA's :180917
## ransompaidus ransomnote hostkidoutcome hostkidoutcome_txt
## Min. : -99.0 Length:181691 Min. :1.00 Length:181691
## 1st Qu.: 0.0 Class :character 1st Qu.:2.00 Class :character
## Median : 0.0 Mode :character Median :4.00 Mode :character
## Mean : 240.4 Mean :4.63
## 3rd Qu.: 0.0 3rd Qu.:7.00
## Max. :48000.0 Max. :7.00
## NA's :181139 NA's :170700
## nreleased addnotes scite1 scite2
## Min. : -99.00 Length:181691 Length:181691 Length:181691
## 1st Qu.: -99.00 Class :character Class :character Class :character
## Median : 0.00 Mode :character Mode :character Mode :character
## Mean : -29.02
## 3rd Qu.: 1.00
## Max. :2769.00
## NA's :171291
## scite3 dbsource INT_LOG INT_IDEO
## Length:181691 Length:181691 Min. :-9.000 Min. :-9.000
## Class :character Class :character 1st Qu.:-9.000 1st Qu.:-9.000
## Mode :character Mode :character Median :-9.000 Median :-9.000
## Mean :-4.544 Mean :-4.464
## 3rd Qu.: 0.000 3rd Qu.: 0.000
## Max. : 1.000 Max. : 1.000
##
## INT_MISC INT_ANY related
## Min. :-9.00000 Min. :-9.000 Length:181691
## 1st Qu.: 0.00000 1st Qu.:-9.000 Class :character
## Median : 0.00000 Median : 0.000 Mode :character
## Mean : 0.09001 Mean :-3.946
## 3rd Qu.: 0.00000 3rd Qu.: 0.000
## Max. : 1.00000 Max. : 1.000
##
sapply(raw_data, function(x) sum(is.na(x)))
## eventid iyear imonth iday
## 0 0 0 0
## approxdate extended resolution country
## 0 0 0 0
## country_txt region region_txt provstate
## 0 0 0 0
## city latitude longitude specificity
## 0 4556 4557 6
## vicinity location summary crit1
## 0 0 0 0
## crit2 crit3 doubtterr alternative
## 0 0 1 152680
## alternative_txt multiple success suicide
## 0 1 0 0
## attacktype1 attacktype1_txt attacktype2 attacktype2_txt
## 0 0 175377 0
## attacktype3 attacktype3_txt targtype1 targtype1_txt
## 181263 0 0 0
## targsubtype1 targsubtype1_txt corp1 target1
## 10373 0 33 1
## natlty1 natlty1_txt targtype2 targtype2_txt
## 1559 0 170547 0
## targsubtype2 targsubtype2_txt corp2 target2
## 171006 0 0 0
## natlty2 natlty2_txt targtype3 targtype3_txt
## 170863 0 180515 0
## targsubtype3 targsubtype3_txt corp3 target3
## 180594 0 0 0
## natlty3 natlty3_txt gname gsubname
## 180544 0 0 0
## gname2 gsubname2 gname3 gsubname3
## 0 0 0 0
## motive guncertain1 guncertain2 guncertain3
## 0 380 179736 181371
## individual nperps nperpcap claimed
## 0 71115 69489 66120
## claimmode claimmode_txt claim2 claimmode2
## 162608 0 179801 181075
## claimmode2_txt claim3 claimmode3 claimmode3_txt
## 0 181373 181558 0
## compclaim weaptype1 weaptype1_txt weapsubtype1
## 176852 0 0 20768
## weapsubtype1_txt weaptype2 weaptype2_txt weapsubtype2
## 0 168564 0 170149
## weapsubtype2_txt weaptype3 weaptype3_txt weapsubtype3
## 0 179828 0 179998
## weapsubtype3_txt weaptype4 weaptype4_txt weapsubtype4
## 0 181618 0 181621
## weapsubtype4_txt weapdetail nkill nkillus
## 0 0 10313 64446
## nkillter nwound nwoundus nwoundte
## 66958 16311 64702 69143
## property propextent propextent_txt propvalue
## 0 117626 0 142702
## propcomment ishostkid nhostkid nhostkidus
## 0 178 168119 168174
## nhours ndays divert kidhijcountry
## 177628 173567 0 0
## ransom ransomamt ransomamtus ransompaid
## 104310 180341 181128 180917
## ransompaidus ransomnote hostkidoutcome hostkidoutcome_txt
## 181139 0 170700 0
## nreleased addnotes scite1 scite2
## 171291 0 0 0
## scite3 dbsource INT_LOG INT_IDEO
## 0 0 0 0
## INT_MISC INT_ANY related
## 0 0 0
data2[is.na(data2)] <- 0
sapply(data2, function(x) sum(is.na(x)))
## eventid iyear imonth iday
## 0 0 0 0
## approxdate extended resolution country
## 0 0 0 0
## country_txt region region_txt provstate
## 0 0 0 0
## city latitude longitude specificity
## 0 0 0 0
## vicinity location summary crit1
## 0 0 0 0
## crit2 crit3 doubtterr alternative
## 0 0 0 0
## alternative_txt multiple success suicide
## 0 0 0 0
## attacktype1 attacktype1_txt attacktype2 attacktype2_txt
## 0 0 0 0
## attacktype3 attacktype3_txt targtype1 targtype1_txt
## 0 0 0 0
## targsubtype1 targsubtype1_txt corp1 target1
## 0 0 0 0
## natlty1 natlty1_txt targtype2 targtype2_txt
## 0 0 0 0
## targsubtype2 targsubtype2_txt corp2 target2
## 0 0 0 0
## natlty2 natlty2_txt targtype3 targtype3_txt
## 0 0 0 0
## targsubtype3 targsubtype3_txt corp3 target3
## 0 0 0 0
## natlty3 natlty3_txt gname gsubname
## 0 0 0 0
## gname2 gsubname2 gname3 gsubname3
## 0 0 0 0
## motive guncertain1 guncertain2 guncertain3
## 0 0 0 0
## individual nperps nperpcap claimed
## 0 0 0 0
## claimmode claimmode_txt claim2 claimmode2
## 0 0 0 0
## claimmode2_txt claim3 claimmode3 claimmode3_txt
## 0 0 0 0
## compclaim weaptype1 weaptype1_txt weapsubtype1
## 0 0 0 0
## weapsubtype1_txt weaptype2 weaptype2_txt weapsubtype2
## 0 0 0 0
## weapsubtype2_txt weaptype3 weaptype3_txt weapsubtype3
## 0 0 0 0
## weapsubtype3_txt weaptype4 weaptype4_txt weapsubtype4
## 0 0 0 0
## weapsubtype4_txt weapdetail nkill nkillus
## 0 0 0 0
## nkillter nwound nwoundus nwoundte
## 0 0 0 0
## property propextent propextent_txt propvalue
## 0 0 0 0
## propcomment ishostkid nhostkid nhostkidus
## 0 0 0 0
## nhours ndays divert kidhijcountry
## 0 0 0 0
## ransom ransomamt ransomamtus ransompaid
## 0 0 0 0
## ransompaidus ransomnote hostkidoutcome hostkidoutcome_txt
## 0 0 0 0
## nreleased addnotes scite1 scite2
## 0 0 0 0
## scite3 dbsource INT_LOG INT_IDEO
## 0 0 0 0
## INT_MISC INT_ANY related
## 0 0 0
data2$attack_var <- data2$nkill
data2 <- data2 %>% mutate(attack_var = replace(attack_var,data2$nkill>=3& data2$nkill<10,2))
data2 <- data2 %>% mutate(attack_var = replace(attack_var, data2$nkill>=10,3))
data2 <- data2 %>% mutate(attack_var = replace(attack_var, data2$nkill<3,1))
df1 <- data2[data2$attack_var == "1", ]
df2<- data2[data2$attack_var == "2", ]
df3<-data2[data2$attack_var == "3", ]
df1_num = data.frame(year=1970,df1 %>% filter(df1$iyear==1970) %>% summarize(attacks = n()))
df2_num = data.frame(year=1970,df2 %>% filter(df2$iyear==1970) %>% summarize(attacks = n()))
df3_num = data.frame(year=1970,df3 %>% filter(df3$iyear==1970) %>% summarize(attacks = n()))
for (n in 1971:2017)
{
df1_num[nrow(df1_num) + 1,] = c(n, df1 %>% filter(df1$iyear==n) %>% summarize(attacks = n()))
df2_num[nrow(df2_num) + 1,] = c(n, df2 %>% filter(df2$iyear==n) %>% summarize(attacks = n()))
df3_num[nrow(df3_num) + 1,] = c(n, df3 %>% filter(df3$iyear==n) %>% summarize(attacks = n()))
}
library(ggplot2)
ggplot(df1_num, aes(x=year, y=attacks)) + geom_point()+ ggtitle ("Minor attacks") +
theme(text=element_text(size=16))
ggplot(df2_num, aes(x=year, y=attacks)) + geom_point() + ggtitle ("Mid-sized attacks") + theme(text=element_text(size=16))
ggplot(df3_num, aes(x=year, y=attacks)) + geom_point() + ggtitle ("Major attacks") + theme(text=element_text(size=16))
m1 <- lm(attacks ~ year, data=df1_num)
summary(m1)
##
## Call:
## lm(formula = attacks ~ year, data = df1_num)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3812.6 -1227.2 426.5 938.1 7588.4
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -278182.47 49616.07 -5.607 1.12e-06 ***
## year 141.10 24.89 5.669 9.03e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2389 on 46 degrees of freedom
## Multiple R-squared: 0.4113, Adjusted R-squared: 0.3985
## F-statistic: 32.14 on 1 and 46 DF, p-value: 9.035e-07
par(mfrow= c(2,2))
plot(m1)
m2 <- lm(attacks ~ year, data=df2_num)
summary(m2)
##
## Call:
## lm(formula = attacks ~ year, data = df2_num)
##
## Residuals:
## Min 1Q Median 3Q Max
## -531.28 -358.27 58.38 144.83 1396.87
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -57296.024 8438.849 -6.790 1.89e-08 ***
## year 28.985 4.233 6.847 1.54e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 406.3 on 46 degrees of freedom
## Multiple R-squared: 0.5048, Adjusted R-squared: 0.494
## F-statistic: 46.89 on 1 and 46 DF, p-value: 1.545e-08
par(mfrow= c(2,2))
plot(m2)
m3 <- lm(attacks ~ year, data=df3_num)
summary(m3)
##
## Call:
## lm(formula = attacks ~ year, data = df3_num)
##
## Residuals:
## Min 1Q Median 3Q Max
## -214.95 -127.53 6.84 43.26 544.24
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -20066.926 3278.105 -6.122 1.90e-07 ***
## year 10.162 1.644 6.180 1.55e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 157.8 on 46 degrees of freedom
## Multiple R-squared: 0.4536, Adjusted R-squared: 0.4418
## F-statistic: 38.19 on 1 and 46 DF, p-value: 1.555e-07
par(mfrow= c(2,2))
plot(m3)
ggplot(df1_num, aes(x=year, y=attacks)) + geom_point() + ggtitle ("Minor attacks (LM)") + theme(text=element_text(size=16)) +
stat_smooth(method = "lm",
formula = y ~ x,
geom = "smooth")
ggplot(df2_num, aes(x=year, y=attacks)) + geom_point() + ggtitle ("Mid sized attacks (LM)") + theme(text=element_text(size=16)) +
stat_smooth(method = "lm",
formula = y ~ x,
geom = "smooth")
ggplot(df3_num, aes(x=year, y=attacks)) + geom_point() + ggtitle ("Major attacks (LM)") + theme(text=element_text(size=16)) +
stat_smooth(method = "lm",
formula = y ~ x,
geom = "smooth")
colors <- c("Major Attacks"="red","Small Attacks"="blue","Minor Attacks"="green")
ggplot()+
geom_point(data=df1_num, aes(x=year,y=attacks,color="Major Attacks"), show.legend = TRUE)+
geom_point(data=df2_num, aes(x=year,y=attacks,color="Minor Attacks"),show.legend = TRUE)+
geom_point(data=df3_num, aes(x=year,y=attacks,color="Small Attacks"),show.legend = TRUE)+
ggtitle('Three types of terror attacks') +
labs(color = "Type of Attack")+
scale_color_manual(values = colors)+
scale_x_continuous("Year of Attacks")+
scale_y_continuous("Number of Attacks")
library(tidyverse)
library(dplyr)
library(ggplot2)
world_data <- raw_data
world_data1 <- world_data[!is.na(world_data$latitude),]
world_data2 <- world_data1[!is.na(world_data1$longitude),]
world_data2$attack_var <- world_data2$nkill
world_data2 <- world_data2 %>% mutate(attack_var = replace(attack_var, world_data2$nkill>=3 & world_data2$nkill<10,2))
world_data2 <- world_data2 %>% mutate(attack_var = replace(attack_var, world_data2$nkill>=10,3))
world_data2 <- world_data2 %>% mutate(attack_var = replace(attack_var, world_data2$nkill<3,1))
world_data2[is.na(world_data2)] <- 0
wd1 <- world_data2[world_data2$attack_var == "1", ]
wd2 <- world_data2[world_data2$attack_var == "2", ]
wd3 <- world_data2[world_data2$attack_var == "3", ]
world_coordinates <- map_data("world")
options(repr.plot.width = 50, repr.plot.height =50)
ggplot() + geom_map(
data = world_coordinates, map = world_coordinates,
aes(long, lat, map_id = region), fill="grey"
) +
geom_point(
data = wd1,
aes(x=longitude, y=latitude, size='attack_num')
)
## Warning in geom_map(data = world_coordinates, map = world_coordinates,
## aes(long, : Ignoring unknown aesthetics: x and y
## Warning: Using size for a discrete variable is not advised.
library(ggmap)
## ℹ Google's Terms of Service: <https://mapsplatform.google.com>
## ℹ Please cite ggmap if you use it! Use `citation("ggmap")` for details.
world <- map_data("world")
Major attacks
ggplot() +
geom_map(
data = world, map = world,
aes(long, lat, map_id = region),
color = "white", fill = "lightgray", size = 0.1
) +
geom_point(
data = df1,
aes(longitude, latitude,
color = nkill),
alpha = 0.5
) +
labs(x = NULL, y = NULL, color = NULL)+
theme_void() +
theme(legend.position = "none")+
labs(title="Major Terror Attack Locations")
## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
## Warning in geom_map(data = world, map = world, aes(long, lat, map_id =
## region), : Ignoring unknown aesthetics: x and y
plot(x=df1_num$year,y=df1_num$attacks,main = "Major Terror Attacks",
xlab = "Year",
ylab = "Number of Attacks")
abline(m1,col = "red")
library(car)
## Loading required package: carData
##
## Attaching package: 'car'
## The following object is masked from 'package:dplyr':
##
## recode
## The following object is masked from 'package:purrr':
##
## some
# Calculating Residuals
residuals <- m1$residuals
# Normality test
ks.test(residuals, rnorm(100,0,1)) # Since p value is very small, we can reject the null hypothesis
##
## Exact two-sample Kolmogorov-Smirnov test
##
## data: residuals and rnorm(100, 0, 1)
## D = 0.60417, p-value = 1.275e-11
## alternative hypothesis: two-sided
# And say that residuals do not follow a normal distritbution
shapiro.test(residuals) # Small value of Shapiro-Wilk test also leads up to the same result
##
## Shapiro-Wilk normality test
##
## data: residuals
## W = 0.90946, p-value = 0.001288
# Constant Variance Assumption/Cook Weinberg Test
ncvTest(m1) # Small value indicates that assumption is violated
## Non-constant Variance Score Test
## Variance formula: ~ fitted.values
## Chisquare = 24.3818, Df = 1, p = 7.9012e-07
plot(m1$fitted.values, m1$residuals)
# Spread Level Plot
myspread <- spreadLevelPlot(m1)
## Warning in spreadLevelPlot.lm(m1):
## 2 negative fitted values removed
myspread
##
## Suggested power transformation: 0.2399187
y <- df1_num$attacks
x <- df1_num$year
# Spread Level transformation and New Model
z<-y^(myspread$PowerTransformation)
mylm2<-lm(z ~ x)
summary(mylm2)
##
## Call:
## lm(formula = z ~ x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.2990 -0.5286 0.1160 0.8684 2.1307
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -124.61586 28.76360 -4.332 7.93e-05 ***
## x 0.06569 0.01443 4.553 3.88e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.385 on 46 degrees of freedom
## Multiple R-squared: 0.3106, Adjusted R-squared: 0.2956
## F-statistic: 20.73 on 1 and 46 DF, p-value: 3.876e-05
# Cook Weinberg Test Again
ncvTest(mylm2) # a high value of p indicates constant variance
## Non-constant Variance Score Test
## Variance formula: ~ fitted.values
## Chisquare = 1.692439, Df = 1, p = 0.19328
plot(mylm2$fitted.values, mylm2$residuals)
# Normality Test
residuals2 <- mylm2$residuals
n <- 47
sd1 <- sd(mylm2$residuals)
ks.test(rnorm(n,0,sd1),mylm2$residuals) # Large value of p indicates that residuals are normal
##
## Exact two-sample Kolmogorov-Smirnov test
##
## data: rnorm(n, 0, sd1) and mylm2$residuals
## D = 0.15691, p-value = 0.5339
## alternative hypothesis: two-sided
Minor attacks
ggplot() +
geom_map(
data = world, map = world,
aes(long, lat, map_id = region),
color = "white", fill = "lightgray", size = 0.1
) +
geom_point(
data = df2,
aes(longitude, latitude,
color = nkill),
alpha = 0.5
) +
labs(x = NULL, y = NULL, color = NULL)+
theme_void() +
theme(legend.position = "none")+
labs(title="Minor Terror Attack Locations")
## Warning in geom_map(data = world, map = world, aes(long, lat, map_id =
## region), : Ignoring unknown aesthetics: x and y
plot(x=df2_num$year,y=df2_num$attacks,main = "Minor Terror Attacks",
xlab = "Year",
ylab = "Number of Attacks")
abline(m2,col = "red")
# Calculating Residuals
residuals <- m2$residuals
# Normality test
ks.test(residuals, rnorm(100,0,1)) # Since p value is very small, we can reject the null hypothesis
##
## Exact two-sample Kolmogorov-Smirnov test
##
## data: residuals and rnorm(100, 0, 1)
## D = 0.60417, p-value = 1.275e-11
## alternative hypothesis: two-sided
# And say that residuals do not follow a normal distritbution
shapiro.test(residuals) # Small value of Shapiro-Wilk test also leads up to the same result
##
## Shapiro-Wilk normality test
##
## data: residuals
## W = 0.8801, p-value = 0.0001522
# Constant Variance Assumption/Cook Weinberg Test
ncvTest(m2) # Small value indicates that assumption is violated
## Non-constant Variance Score Test
## Variance formula: ~ fitted.values
## Chisquare = 28.48745, Df = 1, p = 9.4308e-08
plot(m2$fitted.values, m2$residuals)
# Spread Level Plot
myspread <- spreadLevelPlot(m2)
## Warning in spreadLevelPlot.lm(m2):
## 7 negative fitted values removed
myspread
##
## Suggested power transformation: 0.1683115
y <- df2_num$attacks
x <- df2_num$year
# Spread Level transformation and New Model
z<-y^(myspread$PowerTransformation)
mylm2<-lm(z ~ x)
summary(mylm2)
##
## Call:
## lm(formula = z ~ x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.51658 -0.28888 0.03398 0.36740 0.51480
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -67.25423 10.14640 -6.628 3.29e-08 ***
## x 0.03501 0.00509 6.878 1.39e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4885 on 46 degrees of freedom
## Multiple R-squared: 0.507, Adjusted R-squared: 0.4963
## F-statistic: 47.31 on 1 and 46 DF, p-value: 1.388e-08
# Cook Weinberg Test Again
ncvTest(mylm2) # a high value of p indicates constant variance
## Non-constant Variance Score Test
## Variance formula: ~ fitted.values
## Chisquare = 0.2236473, Df = 1, p = 0.63627
plot(mylm2$fitted.values, mylm2$residuals)
# Normality Test
residuals2 <- mylm2$residuals
n <- 47
sd1 <- sd(mylm2$residuals)
ks.test(rnorm(n,0,sd1),mylm2$residuals) # Large value of p indicates that residuals are normal
##
## Exact two-sample Kolmogorov-Smirnov test
##
## data: rnorm(n, 0, sd1) and mylm2$residuals
## D = 0.19193, p-value = 0.3017
## alternative hypothesis: two-sided
Small attacks
ggplot() +
geom_map(
data = world, map = world,
aes(long, lat, map_id = region),
color = "white", fill = "lightgray", size = 0.1
) +
geom_point(
data = df3,
aes(longitude, latitude,
color = nkill),
alpha = 0.5
) +
labs(x = NULL, y = NULL, color = NULL)+
theme_void() +
theme(legend.position = "none")+
labs(title="Small Terror Attack Locations")
## Warning in geom_map(data = world, map = world, aes(long, lat, map_id =
## region), : Ignoring unknown aesthetics: x and y
plot(x=df3_num$year,y=df3_num$attacks,main = "Small Terror Attacks",
xlab = "Year",
ylab = "Number of Attacks")
abline(m3,col = "red")
# Calculating Residuals
residuals <- m3$residuals
# Normality test
ks.test(residuals, rnorm(100,0,1)) # Since p value is very small, we can reject the null hypothesis
##
## Exact two-sample Kolmogorov-Smirnov test
##
## data: residuals and rnorm(100, 0, 1)
## D = 0.52083, p-value = 1.384e-08
## alternative hypothesis: two-sided
# And say that residuals do not follow a normal distritbution
shapiro.test(residuals) # Small value of Shapiro-Wilk test also leads up to the same result
##
## Shapiro-Wilk normality test
##
## data: residuals
## W = 0.85464, p-value = 2.895e-05
# Constant Variance Assumption/Cook Weinberg Test
ncvTest(m3) # Small value indicates that assumption is violated
## Non-constant Variance Score Test
## Variance formula: ~ fitted.values
## Chisquare = 29.20809, Df = 1, p = 6.5007e-08
plot(m3$fitted.values, m3$residuals)
# Spread Level Plot
myspread <- spreadLevelPlot(m3)
## Warning in spreadLevelPlot.lm(m3):
## 5 negative fitted values removed
myspread
##
## Suggested power transformation: 0.2107483
y <- df3_num$attacks
x <- df3_num$year
# Spread Level transformation and New Model
z<-y^(myspread$PowerTransformation)
mylm2<-lm(z ~ x)
summary(mylm2)
##
## Call:
## lm(formula = z ~ x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.6487 -0.3364 -0.0645 0.4596 0.9958
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -85.621139 12.558213 -6.818 1.71e-08 ***
## x 0.044290 0.006299 7.031 8.19e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6046 on 46 degrees of freedom
## Multiple R-squared: 0.518, Adjusted R-squared: 0.5075
## F-statistic: 49.43 on 1 and 46 DF, p-value: 8.193e-09
# Cook Weinberg Test Again
ncvTest(mylm2) # a high value of p indicates constant variance
## Non-constant Variance Score Test
## Variance formula: ~ fitted.values
## Chisquare = 0.7836975, Df = 1, p = 0.37601
plot(mylm2$fitted.values, mylm2$residuals)
# Normality Test
residuals2 <- mylm2$residuals
n <- 47
sd1 <- sd(mylm2$residuals)
ks.test(rnorm(n,0,sd1),mylm2$residuals) # Large value of p indicates that residuals are normal
##
## Exact two-sample Kolmogorov-Smirnov test
##
## data: rnorm(n, 0, sd1) and mylm2$residuals
## D = 0.2141, p-value = 0.1972
## alternative hypothesis: two-sided